In clinical trials that involve imaging, there is a need to upload data into a validated computer system for storage and/or analysis, and in doing so, to ensure that the data is compliant with any data privacy legislation, that it is “clean” (i.e. correctly labelled and error free), and that any problem data is flagged, and that relevant parties are notified. All handling of the images must be compliant with relevant regulations (e.g. ICH-GCP and 21 cfr pt 11).
Clinical image data is frequently transferred as DICOM format, either across a network or on removable media. The DICOM image format includes the image, and also a header containing metadata that relates to the subject and the acquired images. For clinical trial use, it is accompanied by a paper or electronic form that contains associated information which is not included in the DICOM metadata (e.g. the trial identifier, time-point in the trial, any comments made by the collecting site), and may also be accompanied by other data files. While DICOM provides a standard format for image transfer and storage, it does not standardize many of the components of the DICOM header (which includes a number of tags), which are entered by the person operating the scanner. Such tags include the Series Description, which defines the type of scan (e.g. T1 weighted vs T2 weighted MRI scan) and will invariably depend on the language spoken in the country where the data is collected, procedures at that site, and is additionally prone to human error. Furthermore, the DICOM tags do not contain certain relevant information required in clinical trials, such as a precise description of the anatomy imaged (so that checks can be made that the correct anatomy was imaged), the compound being used or its mode of action, and many types of data required for the quantitative analysis of image data. Also, while DICOM is widely used, it is not a universally supported format—especially for analysed results—and so other formats need to be handled also, and these have different ways of storing metadata, sometimes primarily in the file name and folder names that contain these files.
Current methods for importing clinical trial data require substantial user interaction, manual resolution or correction of ambiguities in the metadata (which we refer to as correction of mis-labelling errors), and visual identification of problem data. Systems are available that check the DICOM metadata for conformance, but the DICOM data alone does not provide all the relevant information (e.g. visit number), and other associated files that are needed to complete the analysis, or that result from the analysis, are often not in DICOM format. In many cases mis-labellings or incomplete data will go undetected resulting in erroneous results being included in the analysis, and these errors may not be detected before the results are used in decision making or are submitted to regulators for the approval of the drug.
Current methods for storing image data are also not amenable to aggregation of data from multiple trials for re-analysis or meta-analysis, and to achieve this, it is necessary to add additional metadata on import so that searches across trials can be performed, eg: on mode of action of drugs, pathology recruited etc.
With the advent of personalized healthcare, it is becoming increasingly common for patients to be imaged multiple times as part of diagnosis or treatment protocols. The same challenges arise in these circumstances as arise in clinical trials, especially since a patient's images are unlikely to all be collected on the same scanner or even at the same hospital. Also aggregation of data from numerous patients treated at multiple hospitals has benefits for clinical audit evidence based medicine.